Evaluation of Online Learning Modules for Improving Physical Activity Counseling Skills, Practices, and Knowledge of Oncology Nurses
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To examine the effectiveness of online learning modules for improving physical activity counseling practices among oncology nurses. . DESIGN: Randomized, controlled trial. . SETTING: Online. . SAMPLE: 54 oncology nurses. . METHODS: Oncology nurses were randomly assigned to the learning modules group or control group. The learning modules group completed six online learning modules and quizzes focused on physical activity for cancer survivors, general physical activity principles, and motivational interviewing. . MAIN RESEARCH VARIABLES: Percentage of cancer survivors counseled, self-efficacy for physical activity counseling, knowledge of physical activity, and perceived barriers and benefits of physical activity counseling. . FINDINGS: Analyses of covariance revealed no significant difference between the learning modules and control groups in the percentage of cancer survivors that oncology nurses counseled. Significant differences were found in self-efficacy for physical activity counseling and perceived barriers to physical activity counseling at postintervention. . CONCLUSIONS: The online learning intervention tested in this study improved some parameters of physical activity counseling but did not increase the percentage of cancer survivors that oncology nurses counseled. Additional pilot work is needed to refine the intervention. . IMPLICATIONS FOR NURSING: This study suggests the potential utility of an evidence-based online learning strategy for oncology nurses that includes information on physical activity and its benefits in cancer survivorship. The findings offer a framework on how to implement physical activity counseling skills in oncology nursing practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".